class_docstr: Manages label issues in a Datalab.
  
  Parameters
  ----------
  datalab :
      A Datalab instance.
  
  k :
      The number of nearest neighbors to consider when computing pred_probs from features.
      Only applicable if features are provided and pred_probs are not.
  
  clean_learning_kwargs :
      Keyword arguments to pass to the :py:meth:`CleanLearning <cleanlab.classification.CleanLearning>` constructor.
  
  health_summary_parameters :
      Keyword arguments to pass to the :py:meth:`health_summary <cleanlab.dataset.health_summary>` function.
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docstr: Reset the attributes of this manager based on the available datalab info
  and the keyword arguments stored as instance attributes.
  
  This allows the builder to use pre-computed info from the datalab to speed up
  some computations in the :py:meth:`find_issues` method.
docstr_tok: ["Reset","the","attributes","of","this","manager","based","on","the","available","datalab","info","and","the","keyword","arguments","stored","as","instance","attributes",".","This","allows","the","builder","to","use","pre-computed","info","from","the","datalab","to","speed","up","some","computations","in","the",":","py",":","meth",":","`","find_issues","`","method","."]
code: def _reset(self) -> None:
      """Reset the attributes of this manager based on the available datalab info
      and the keyword arguments stored as instance attributes.
  
      This allows the builder to use pre-computed info from the datalab to speed up
      some computations in the :py:meth:`find_issues` method.
      """
      if not self.health_summary_parameters:
          statistics_dict = self.datalab.get_info("statistics")
          self.health_summary_parameters = {
              "labels": self.datalab.labels,
              "class_names": list(self.datalab._label_map.values()),
              "num_examples": statistics_dict.get("num_examples"),
              "joint": statistics_dict.get("joint", None),
              "confident_joint": statistics_dict.get(
                  "confident_joint", None
              ),
              "multi_label": statistics_dict.get("multi_label", None),
              "asymmetric": statistics_dict.get("asymmetric", None),
              "verbose": False,
          }
      self.health_summary_parameters = {
          k: v
          for k, v in self.health_summary_parameters.items()
          if v is not None
      }
  
file_path: cleanlab/cleanlab/datalab/internal/issue_manager/label.py
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.preprocessing import OneHotEncoder
  import numpy
  from cleanlab.classification import CleanLearning
  from cleanlab.datalab.internal.issue_manager import IssueManager
  from cleanlab.internal.validation import assert_valid_inputs
label_desc: Use image node_id 3 for calling the LabelIssueManager obj's underlying member method code with example usage: obj._reset() without return types